import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation




def 							UpperCamelCase		(			__lowercase		:					int      ):
		'''simple docstring'''




		A_						:							List[Any]      = 3_84
		if "tiny" in model_name:
				A_						:							List[Any]      = [3, 3, 9, 3]
				A_						:							List[Any]      = [96, 1_92, 3_84, 7_68]
		if "small" in model_name:
				A_						:							Optional[int]      = [3, 3, 27, 3]
				A_						:							Tuple      = [96, 1_92, 3_84, 7_68]
		if "base" in model_name:
				A_						:							Optional[int]      = [3, 3, 27, 3]
				A_						:							Optional[int]      = [1_28, 2_56, 5_12, 10_24]
				A_						:							str      = 5_12
		if "large" in model_name:
				A_						:							Any      = [3, 3, 27, 3]
				A_						:							str      = [1_92, 3_84, 7_68, 15_36]
				A_						:							List[str]      = 7_68
		if "xlarge" in model_name:
				A_						:							Optional[int]      = [3, 3, 27, 3]
				A_						:							Dict      = [2_56, 5_12, 10_24, 20_48]
				A_						:							List[Any]      = 10_24

		# set label information
		A_						:							Union[str, Any]      = 1_50
		A_						:							Union[str, Any]      = 'huggingface/label-files'
		A_						:							Union[str, Any]      = 'ade20k-id2label.json'
		A_						:							Optional[Any]      = json.load(open(hf_hub_download(__lowercase		,__lowercase		,repo_type='dataset'      )		,'r'      )      )
		A_						:							Optional[int]      = {int(__lowercase      ): v for k, v in idalabel.items()}
		A_						:							Union[str, Any]      = {v: k for k, v in idalabel.items()}

		A_						:							Tuple      = ConvNextConfig(
		    depths=__lowercase		,hidden_sizes=__lowercase		,out_features=['stage1', 'stage2', 'stage3', 'stage4']      )
		A_						:							Tuple      = UperNetConfig(
		    backbone_config=__lowercase		,auxiliary_in_channels=__lowercase		,num_labels=__lowercase		,idalabel=__lowercase		,labelaid=__lowercase		,)

		return config




def 							UpperCamelCase		(			__lowercase		:					Optional[int]      ):
		'''simple docstring'''




		A_						:							Tuple      = []

		# fmt: off
		# stem
		rename_keys.append(('backbone.downsample_layers.0.0.weight', 'backbone.embeddings.patch_embeddings.weight')      )
		rename_keys.append(('backbone.downsample_layers.0.0.bias', 'backbone.embeddings.patch_embeddings.bias')      )
		rename_keys.append(('backbone.downsample_layers.0.1.weight', 'backbone.embeddings.layernorm.weight')      )
		rename_keys.append(('backbone.downsample_layers.0.1.bias', 'backbone.embeddings.layernorm.bias')      )
		# stages
		for i in range(len(config.backbone_config.depths      )      ):
				for j in range(config.backbone_config.depths[i]      ):
						rename_keys.append((f'''backbone.stages.{i}.{j}.gamma''', f'''backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter''')      )
						rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.weight''')      )
						rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.bias''')      )
						rename_keys.append((f'''backbone.stages.{i}.{j}.norm.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.weight''')      )
						rename_keys.append((f'''backbone.stages.{i}.{j}.norm.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.bias''')      )
						rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight''')      )
						rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias''')      )
						rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight''')      )
						rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias''')      )
				if i > 0:
						rename_keys.append((f'''backbone.downsample_layers.{i}.0.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.weight''')      )
						rename_keys.append((f'''backbone.downsample_layers.{i}.0.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.bias''')      )
						rename_keys.append((f'''backbone.downsample_layers.{i}.1.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.weight''')      )
						rename_keys.append((f'''backbone.downsample_layers.{i}.1.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.bias''')      )

				rename_keys.append((f'''backbone.norm{i}.weight''', f'''backbone.hidden_states_norms.stage{i+1}.weight''')      )
				rename_keys.append((f'''backbone.norm{i}.bias''', f'''backbone.hidden_states_norms.stage{i+1}.bias''')      )

		# decode head
		rename_keys.extend(
		    [
		        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
		        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
		        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
		        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
		    ]      )
		# fmt: on

		return rename_keys




def 							UpperCamelCase		(			__lowercase		:					Dict		,__lowercase		:					Optional[Any]		,__lowercase		:					Tuple      ):
		'''simple docstring'''




		A_						:							Any      = dct.pop(__lowercase      )
		A_						:							Any      = val




def 							UpperCamelCase		(			__lowercase		:					List[str]		,__lowercase		:					Union[str, Any]		,__lowercase		:					str      ):
		'''simple docstring'''




		A_						:							Any      = {
		    'upernet-convnext-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth',
		    'upernet-convnext-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth',
		    'upernet-convnext-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth',
		    'upernet-convnext-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth',
		    'upernet-convnext-xlarge': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth',
		}
		A_						:							Union[str, Any]      = model_name_to_url[model_name]
		A_						:							Any      = torch.hub.load_state_dict_from_url(__lowercase		,map_location='cpu'      )['state_dict']

		A_						:							int      = get_upernet_config(__lowercase      )
		A_						:							int      = UperNetForSemanticSegmentation(__lowercase      )
		model.eval()

		# replace "bn" => "batch_norm"
		for key in state_dict.copy().keys():
				A_						:							List[str]      = state_dict.pop(__lowercase      )
				if "bn" in key:
						A_						:							Tuple      = key.replace('bn'		,'batch_norm'      )
				A_						:							str      = val

		# rename keys
		A_						:							Union[str, Any]      = create_rename_keys(__lowercase      )
		for src, dest in rename_keys:
				rename_key(__lowercase		,__lowercase		,__lowercase      )

		model.load_state_dict(__lowercase      )

		# verify on image
		A_						:							Tuple      = 'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
		A_						:							Union[str, Any]      = Image.open(requests.get(__lowercase		,stream=__lowercase      ).raw      ).convert('RGB'      )

		A_						:							Optional[Any]      = SegformerImageProcessor()
		A_						:							Tuple      = processor(__lowercase		,return_tensors='pt'      ).pixel_values

		with torch.no_grad():
				A_						:							Dict      = model(__lowercase      )

		if model_name == "upernet-convnext-tiny":
				A_						:							Optional[int]      = torch.tensor(
				    [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]      )
		elif model_name == "upernet-convnext-small":
				A_						:							Any      = torch.tensor(
				    [[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]]      )
		elif model_name == "upernet-convnext-base":
				A_						:							Optional[Any]      = torch.tensor(
				    [[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]]      )
		elif model_name == "upernet-convnext-large":
				A_						:							int      = torch.tensor(
				    [[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]]      )
		elif model_name == "upernet-convnext-xlarge":
				A_						:							Optional[Any]      = torch.tensor(
				    [[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]]      )
		print('Logits:'		,outputs.logits[0, 0, :3, :3]      )
		assert torch.allclose(outputs.logits[0, 0, :3, :3]		,__lowercase		,atol=1e-4      )
		print('Looks ok!'      )

		if pytorch_dump_folder_path is not None:
				print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''      )
				model.save_pretrained(__lowercase      )
				print(f'''Saving processor to {pytorch_dump_folder_path}'''      )
				processor.save_pretrained(__lowercase      )

		if push_to_hub:
				print(f'''Pushing model and processor for {model_name} to hub'''      )
				model.push_to_hub(f'''openmmlab/{model_name}'''      )
				processor.push_to_hub(f'''openmmlab/{model_name}'''      )


if __name__ == "__main__":
	_UpperCAmelCase          =						argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""upernet-convnext-tiny""",
	    type=str,
	    choices=[F"""upernet-convnext-{size}""" for size in ["""tiny""", """small""", """base""", """large""", """xlarge"""]],
	    help="""Name of the ConvNext UperNet model you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
	)
	parser.add_argument(
	    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
	)

	_UpperCAmelCase          =						parser.parse_args()
	convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







from __future__ import annotations




def 							UpperCamelCase		(			__lowercase		:					list[int]      ):
		'''simple docstring'''




		A_						:							int      = len(__lowercase      ) // 2

		# choose the middle 3 elements
		A_						:							List[Any]      = lst[m - 1 : m + 2]

		# if middle element is peak
		if three[1] > three[0] and three[1] > three[2]:
				return three[1]

		# if increasing, recurse on right
		elif three[0] < three[2]:
				if len(lst[:m]      ) == 2:
						m -= 1
				return peak(lst[m:]      )

		# decreasing
		else:
				if len(lst[:m]      ) == 2:
						m += 1
				return peak(lst[:m]      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()






